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• W914341734 abstract "IntroductionData Mining in the Gaming IndustryAs more countries and regions are considering legalizing and/or expanding gaming in their respective markets, many casinos in the global casino market are facing increased competition in player acquisition and retention, as well as revenue generation. Hence, customer relationship management (CRM) has become critical for a profitable relationship with customers. One way in which marketers can effectively compete is mining customer data. Mining and analyzing customer data helps marketers better understand customer behavior and predict specific behaviors. This, in turn, will enable them to identify prospects, segment customers, target specific segments, and inevitably optimize the available marketing resources. For these reasons, big data and predictive analytics have become increasingly important across many industries. While some casinos have embraced data analytics and mined customer data for data driven-marketing (Experfy Insights, 2014), to the best knowledge of the authors of this article, there has been relatively little effort in the gaming literature to discuss the application of data mining methods to the prediction of customer behavior.Data mining is generally defined as the process of discovering meaningful patterns, relationships and associations hidden in large data sets by examining and modeling the data (Chung & Gray, 1999; SPSS Inc., an IBM Company, 2010; Peacock, 1998). The application of data mining techniques to customer data has gained increasing attention and popularity from marketers in various industries, especially in the telecommunication and banking sectors. The use of data mining methods in the marketing field has helped marketers target specific groups of customers or individuals and customize their marketing offers (McCarty & Hastak, 2007). Data mining is also used to classify customers who are likely to respond to specific marketing promotions such as direct-mail offers and to identify cross-sell and up-sell opportunities.In the gaming industry, a few anecdotal case studies claim the application of data mining techniques for the analysis of customer data. For example, it was reported that Las Vegas Sands selected SAS, a business intelligence software vendor, for the analysis of the vast amount of customer-related data collected from its multiple gaming facilities (Woodie, 2011). Another company, Harrah's Entertainment Inc., [Caesars Entertainment Corporation] was also reported for its use of data mining techniques and predictive analytics (SAS, 2010). In the academic field, the Division on Addictions at the Cambridge Health Alliance, a Harvard Medical School Teaching Affiliate, has partnered with bwin, one of the major online gaming companies, for collaborative research on the problem gambling detection and responsible gambling initiatives (Burton, 2008). These collaborative research efforts are based on the tracked gaming data of individual online bettors, preventing self-reported biases and recall issues which are typically associated with the self-report questionnaire approach (Burton, 2008). Despite these claims, there has been very little discussion regarding the application of data mining techniques in the gaming industry. Additionally, the authors' experience in the gaming industry confirms the limited application of statistical and data mining methods in customer classification and behavior prediction.Cross-Gaming PredictionOne of the areas where data mining is potentially useful is identifying casino patrons who are likely to play both slots and table games, also known as cross-gaming (Suh & Alhaery, 2014). While it is a common practice in the gaming industry to divide customers into either slot or table game players and market to them accordingly, some patrons may play both slots and table games or have the potential to play both (Suh & Alhaery, 2014). Casino operators often try to promote table games to slot players and vice versa in the hopes of increasing overall gaming revenues (Brokopp, 2008; Green, 2010; Fleming, 2006; Pollard, 2008). …" @default.
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• W914341734 date "2015-06-03" @default.
• W914341734 modified "2023-09-23" @default.
• W914341734 title "PREDICTING CROSS-GAMING PROPENSITY USING E-CHAID ANALYSIS" @default.
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